Monitoring systems including cameras for monitoring of premises, areas of particular interest and/or processes are widely used in order to provide frequently updated images from an image view of interest, i.e. to provide a video sequence of an environment of interest. A problem with monitoring by means of a camera is that the image view from the camera may be covered or in any other way obstructed or altered. For instance, the lens of the camera may be deliberately or unintentionally covered, e.g. by paint, powder, moisture, a piece of cloth, etc., the cameras may be deliberately or unintentionally redirected to present a camera view of no interest, the camera may be removed, or the camera may be severely defocused. In particular tampering of cameras is unwanted in a surveillance situation and can e.g. be an act of vandalism, preparation for a crime or simply produced by carelessness. Either way a surveillance camera can become of limited use when tampered with.
Hence there are a lot of ways a camera view may be obstructed or tampered with resulting in the camera delivering video sequences of no interest or hiding important events. Therefore it is important to automatically alert or raise an alarm when a camera is obstructed or tampered with.
One method for detecting camera dysfunctions is described in Harasse S, et al. “Automated Camera Dysfunction Detection”. Symposium on Image Analysis and Interpretation, 2004. 6th IEEE Southwest, March 2004, pp 36-40. According to this document the dysfunction of a camera is detected by detecting displacement, obstruction or defocusing of the camera. The process of detecting the dysfunction of a camera includes accumulating strong edges from each frame of an image sequence of T frames into a pre-accumulator. Then N pre-accumulators are generated and stored. From the N pre-accumulators a temporal accumulator is generated. The temporal accumulator is updated by generating a new pre-accumulator, subtracting the oldest pre-accumulator from the temporal accumulator, and adding the new pre-accumulator to the temporal accumulator. In order to detect displacement of the camera a reference accumulator, generated in the same way as the temporal accumulator, and a current accumulator are matched using a block matching algorithm, which maximizes the normalized correlation between the two accumulators, and a relative translation between the reference accumulator and the current accumulator is identified. Obstruction is detected by dividing the image space into several blocks and estimating the quantity of information in each block by measuring the entropy. Focus change is detected by computing the gradient energy only where there are stable edges.
The above proposed dysfunction detection requires a lot of memory for storing image frames and different types of accumulators. Moreover, the detection requires a lot of processing capacity and is complex.